An application of near-infrared and mid-infrared spectroscopy to the study of selected minerals

Near-infrared spectroscopy is a somewhat unutilised technique for the study of minerals. The technique has the ability to determine water content, hydroxyl groups and transition metals. In this paper we show the application of NIR spectroscopy to the study of selected minerals. The structure and spectral properties of two Cu-tellurite minerals graemite and teineite are compared with bismuth containing tellurite mineral smirnite by the application of NIR and IR spectroscopy. The position of Cu2+ bands and their splitting in the electronic spectra of tellurites are in conformity with octahedral geometry distortion. The spectral pattern of smirnite resembles graemite and the observed band at 10855 cm-1 with a weak shoulder at 7920 cm-1 is identified as due to Cu2+ ion. Any transition metal impurities may be identified by their bands in this spectral region. Three prominent bands observed in the region of 7200-6500 cm-1 are the overtones of water whilst the weak bands observed near 6200 cm-1in tellurites may be attributed to the hydrogen bonding between (TeO3)2- and H2O. The observation of a number of bands centred at around 7200 cm-1 confirms molecular water in tellurite minerals. A number of overlapping bands in the low wavenumbers 4500-4000 cm-1 is the result of combinational modes of (TeO3)2−ion. The appearance of the most intense peak at 5200 cm-1 with a pair of weak bands near 6000 cm-1 is a common feature in all the spectra and is related to the combinations of OH vibrations of water molecules, and bending vibrations ν2 (δ H2O). Bending vibrations δ H2O observed in the IR spectra shows a single band for smirnite at 1610 cm-1. The resolution of this band into number of components is evidenced for non-equivalent types of molecular water in graemite and teineite. (TeO3)2- stretching vibrations are characterized by three main absorptions at 1080, 780 and 695 cm-1.

Discrepancies between metabolic activity and DNA content as tool to assess cell proliferation in cancer research

Cell proliferation is a critical and frequently studied feature of molecular biology in cancer research. Therefore, various assays are available using different strategies to measure cell proliferation. Metabolic assays such as AlamarBlue, WST-1, and MTT, which were originally developed to determine cell toxicity, are being used to assess cell numbers. Additionally, proliferative activity can be determined by quantification of DNA content using fluorophores, such as CyQuant and PicoGreen. Referring to data published in high ranking cancer journals, 945 publications applied these assays over the past 14 years to examine the proliferative behaviour of diverse cell types. Within this study, mainly metabolic assays were used to quantify changes in cell growth yet these assays may not accurately reflect cellular proliferation rates due to a miscorrelation of metabolic activity and cell number. Testing this hypothesis, we compared metabolic activity of different cell types, human cancer cells and primary cells, over a time period of 4 days using AlamarBlue and fluorometric assays CyQuant and PicoGreen to determine their DNA content. Our results show certain discrepancies in terms of over-estimation of cell proliferation with respect to the metabolic assay in comparison to DNA binding fluorophores.

Exploiting multiple feature sets in data-driven impostor dataset selection for speaker verification

This study assesses the recently proposed data-driven background dataset refinement technique for speaker verification using alternate SVM feature sets to the GMM supervector features for which it was originally designed. The performance improvements brought about in each trialled SVM configuration demonstrate the versatility of background dataset refinement. This work also extends on the originally proposed technique to exploit support vector coefficients as an impostor suitability metric in the data-driven selection process. Using support vector coefficients improved the performance of the refined datasets in the evaluation of unseen data. Further, attempts are made to exploit the differences in impostor example suitability measures from varying features spaces to provide added robustness.

A new multilevel converter configuration for high power and high quality applications

The Queensland University of Technology (QUT) allows the presentation of theses for the Degree of Doctor of Philosophy in the format of published or submitted papers, where such papers have been published, accepted or submitted during the period of candidature. This thesis is composed of ten published /submitted papers and book chapters of which nine have been published and one is under review. This project is financially supported by an Australian Research Council (ARC) Discovery Grant with the aim of investigating multilevel topologies for high quality and high power applications, with specific emphasis on renewable energy systems. The rapid evolution of renewable energy within the last several years has resulted in the design of efficient power converters suitable for medium and high-power applications such as wind turbine and photovoltaic (PV) systems. Today, the industrial trend is moving away from heavy and bulky passive components to power converter systems that use more and more semiconductor elements controlled by powerful processor systems. However, it is hard to connect the traditional converters to the high and medium voltage grids, as a single power switch cannot stand at high voltage. For these reasons, a new family of multilevel inverters has appeared as a solution for working with higher voltage levels. Besides this important feature, multilevel converters have the capability to generate stepped waveforms. Consequently, in comparison with conventional two-level inverters, they present lower switching losses, lower voltage stress across loads, lower electromagnetic interference (EMI) and higher quality output waveforms. These properties enable the connection of renewable energy sources directly to the grid without using expensive, bulky, heavy line transformers. Additionally, they minimize the size of the passive filter and increase the durability of electrical devices. However, multilevel converters have only been utilised in very particular applications, mainly due to the structural limitations, high cost and complexity of the multilevel converter system and control. New developments in the fields of power semiconductor switches and processors will favor the multilevel converters for many other fields of application. The main application for the multilevel converter presented in this work is the front-end power converter in renewable energy systems. Diode-clamped and cascade converters are the most common type of multilevel converters widely used in different renewable energy system applications. However, some drawbacks – such as capacitor voltage imbalance, number of components, and complexity of the control system – still exist, and these are investigated in the framework of this thesis. Various simulations using software simulation tools are undertaken and are used to study different cases. The feasibility of the developments is underlined with a series of experimental results. This thesis is divided into two main sections. The first section focuses on solving the capacitor voltage imbalance for a wide range of applications, and on decreasing the complexity of the control strategy on the inverter side. The idea of using sharing switches at the output structure of the DC-DC front-end converters is proposed to balance the series DC link capacitors. A new family of multioutput DC-DC converters is proposed for renewable energy systems connected to the DC link voltage of diode-clamped converters. The main objective of this type of converter is the sharing of the total output voltage into several series voltage levels using sharing switches. This solves the problems associated with capacitor voltage imbalance in diode-clamped multilevel converters. These converters adjust the variable and unregulated DC voltage generated by renewable energy systems (such as PV) to the desirable series multiple voltage levels at the inverter DC side. A multi-output boost (MOB) converter, with one inductor and series output voltage, is presented. This converter is suitable for renewable energy systems based on diode-clamped converters because it boosts the low output voltage and provides the series capacitor at the output side. A simple control strategy using cross voltage control with internal current loop is presented to obtain the desired voltage levels at the output voltage. The proposed topology and control strategy are validated by simulation and hardware results. Using the idea of voltage sharing switches, the circuit structure of different topologies of multi-output DC-DC converters – or multi-output voltage sharing (MOVS) converters – have been proposed. In order to verify the feasibility of this topology and its application, steady state and dynamic analyses have been carried out. Simulation and experiments using the proposed control strategy have verified the mathematical analysis. The second part of this thesis addresses the second problem of multilevel converters: the need to improve their quality with minimum cost and complexity. This is related to utilising asymmetrical multilevel topologies instead of conventional multilevel converters; this can increase the quality of output waveforms with a minimum number of components. It also allows for a reduction in the cost and complexity of systems while maintaining the same output quality, or for an increase in the quality while maintaining the same cost and complexity. Therefore, the asymmetrical configuration for two common types of multilevel converters – diode-clamped and cascade converters – is investigated. Also, as well as addressing the maximisation of the output voltage resolution, some technical issues – such as adjacent switching vectors – should be taken into account in asymmetrical multilevel configurations to keep the total harmonic distortion (THD) and switching losses to a minimum. Thus, the asymmetrical diode-clamped converter is proposed. An appropriate asymmetrical DC link arrangement is presented for four-level diode-clamped converters by keeping adjacent switching vectors. In this way, five-level inverter performance is achieved for the same level of complexity of the four-level inverter. Dealing with the capacitor voltage imbalance problem in asymmetrical diodeclamped converters has inspired the proposal for two different DC-DC topologies with a suitable control strategy. A Triple-Output Boost (TOB) converter and a Boost 3-Output Voltage Sharing (Boost-3OVS) converter connected to the four-level diode-clamped converter are proposed to arrange the proposed asymmetrical DC link for the high modulation indices and unity power factor. Cascade converters have shown their abilities and strengths in medium and high power applications. Using asymmetrical H-bridge inverters, more voltage levels can be generated in output voltage with the same number of components as the symmetrical converters. The concept of cascading multilevel H-bridge cells is used to propose a fifteen-level cascade inverter using a four-level H-bridge symmetrical diode-clamped converter, cascaded with classical two-level Hbridge inverters. A DC voltage ratio of cells is presented to obtain maximum voltage levels on output voltage, with adjacent switching vectors between all possible voltage levels; this can minimize the switching losses. This structure can save five isolated DC sources and twelve switches in comparison to conventional cascade converters with series two-level H bridge inverters. To increase the quality in presented hybrid topology with minimum number of components, a new cascade inverter is verified by cascading an asymmetrical four-level H-bridge diode-clamped inverter. An inverter with nineteen-level performance was achieved. This synthesizes more voltage levels with lower voltage and current THD, rather than using a symmetrical diode-clamped inverter with the same configuration and equivalent number of power components. Two different predictive current control methods for the switching states selection are proposed to minimise either losses or THD of voltage in hybrid converters. High voltage spikes at switching time in experimental results and investigation of a diode-clamped inverter structure raised another problem associated with high-level high voltage multilevel converters. Power switching components with fast switching, combined with hard switched-converters, produce high di/dt during turn off time. Thus, stray inductance of interconnections becomes an important issue and raises overvoltage and EMI issues correlated to the number of components. Planar busbar is a good candidate to reduce interconnection inductance in high power inverters compared with cables. The effect of different transient current loops on busbar physical structure of the high-voltage highlevel diode-clamped converters is highlighted. Design considerations of proper planar busbar are also presented to optimise the overall design of diode-clamped converters.

Wide-angle visual feature matching for outdoor localization

Wide-angle images exhibit significant distortion for which existing scale-space detectors such as the scale-invariant feature transform (SIFT) are inappropriate. The required scale-space images for feature detection are correctly obtained through the convolution of the image, mapped to the sphere, with the spherical Gaussian. A new visual key-point detector, based on this principle, is developed and several computational approaches to the convolution are investigated in both the spatial and frequency domain. In particular, a close approximation is developed that has comparable computation time to conventional SIFT but with improved matching performance. Results are presented for monocular wide-angle outdoor image sequences obtained using fisheye and equiangular catadioptric cameras. We evaluate the overall matching performance (recall versus 1-precision) of these methods compared to conventional SIFT. We also demonstrate the use of the technique for variable frame-rate visual odometry and its application to place recognition.

Polydipsia : a feature of peritoneal dialysis

Background Some dialysis patients fail to comply with their fluid restriction causing problems due to volume overload. These patients sometimes blame excessive thirst. There has been little work in this area and no work documenting polydipsia among peritoneal dialysis (PD) patients. Methods We measured motivation to drink and fluid consumption in 46 haemodialysis patients (HD), 39 PD patients and 42 healthy controls (HC) using a modified palmtop computer to collect visual analogue scores at hourly intervals. Results Mean thirst scores were markedly depressed on the dialysis day (day 1) for HD (P<0.0001). The profile for day 2 was similar to that of HC. PD generated consistently higher scores than HD day 1 and HC (P = 0.01 vs. HC and P<0.0001 vs HD day 1). Reported mean daily water consumption was similar for HD and PD with both significantly less than HC (P<0.001 for both). However, measured fluid losses were similar for PD and HC whilst HD were lower (P<0.001 for both) suggesting that the PD group may have underestimated their fluid intake. Conclusion Our results indicate that HD causes a protracted period of reduced thirst but that the population's thirst perception is similar to HC on the interdialytic day despite a reduced fluid intake. In contrast, the PD group recorded high thirst scores throughout the day and were apparently less compliant with their fluid restriction. This is potentially important because the volume status of PD patients influences their survival.

Port of Brisbane stormwater quality : stage 1 rainfall simulation to evaluate pollutant build-up and wash-off from selected land uses

Background: The quality of stormwater runoff from ports is significant as it can be an important source of pollution to the marine environment. This is also a significant issue for the Port of Brisbane as it is located in an area of high environmental values. Therefore, it is imperative to develop an in-depth understanding of stormwater runoff quality to ensure that appropriate strategies are in place for quality improvement. ---------------- The Port currently has a network of stormwater sample collection points where event based samples together with grab samples are tested for a range of water quality parameters. Whilst this information provides a ‘snapshot’ of the pollutants being washed from the catchment/s, it does not allow for a quantifiable assessment of total contaminant loads being discharged to the waters of Moreton Bay. It also does not represent pollutant build-up and wash-off from the different land uses across a broader range of rainfall events which might be expected. As such, it is difficult to relate stormwater quality to different pollutant sources within the Port environment. ----------------- Consequently, this would make the source tracking of pollutants to receiving waters extremely difficult and in turn the ability to implement appropriate mitigation measures. Also, without this detailed understanding, the efficacy of the various stormwater quality mitigation measures implemented cannot be determined with certainty. --------------- Current knowledge on port stormwater runoff quality Currently, little knowledge exists with regards to the pollutant generation capacity specific to port land uses as these do not necessarily compare well with conventional urban industrial or commercial land use due to the specific nature of port activities such as inter-modal operations and cargo management. Furthermore, traffic characteristics in a port area are different to a conventional urban area. Consequently, as data inputs based on an industrial and commercial land uses for modelling purposes is questionable. ------------------ A comprehensive review of published research failed to locate any investigations undertaken with regards to pollutant build-up and wash-off for port specific land uses. Furthermore, there is very limited information made available by various ports worldwide about the pollution generation potential of their facilities. Published work in this area has essentially focussed on the water quality or environmental values in the receiving waters such as the downstream bay or estuary. ----------------- The Project: The research project is an outcome of the collaborative Partnership between the Port of Brisbane Corporation (POBC) and Queensland University of Technology (QUT). A key feature of this Partnership is the undertaking of ‘cutting edge’ research to strengthen the environmental custodianship of the Port area. This project aims to develop a port specific stormwater quality model to allow informed decision making in relation to stormwater quality improvement in the context of the increased growth of the Port. --------------- Stage 1 of the research project focussed on the assessment of pollutant build-up and wash-off using rainfall simulation from the current Port of Brisbane facilities with the longer-term objective of contributing to the development of ecological risk mitigation strategies for future expansion scenarios. Investigation of complex processes such as pollutant wash-off using naturally occurring rainfall events has inherent difficulties. These can be overcome using simulated rainfall for the investigations. ----------------- The deliverables for Stage 1 included the following: * Pollutant build-up and wash-off profiles for six primary land uses within the Port of Brisbane to be used for water quality model development. * Recommendations with regards to future stormwater quality monitoring and pollution mitigation measures. The outcomes are expected to deliver the following benefits to the Port of Brisbane: * The availability of Port specific pollutant build-up and wash-off data will enable the implementation of customised stormwater pollution mitigation strategies. * The water quality data collected would form the baseline data for a Port specific water quality model for mitigation and predictive purposes. * To be at the cutting-edge in terms of water quality management and environmental best practice in the context of port infrastructure. ---------------- Conclusions: The important conclusions from the study are: * It confirmed that the Port environment is unique in terms of pollutant characteristics and is not comparable to typical urban land uses. * For most pollutant types, the Port land uses exhibited lower pollutant concentrations when compared to typical urban land uses. * The pollutant characteristics varied across the different land uses and were not consistent in terms of the land use. Hence, the implementation of stereotypical structural water quality improvement devices could be of limited value. * The <150m particle size range was predominant in suspended solids for pollutant build-up as well as wash-off. Therefore, if suspended solids are targeted as the surrogate parameter for water quality improvement, this specific particle size range needs to be removed. ------------------- Recommendations: Based on the study results the following preliminary recommendations are made: * Due to the appreciable variation in pollutant characteristics for different port land uses, any water quality monitoring stations should preferably be located such that source areas can be easily identified. * The study results having identified significant pollutants for the different land uses should enable the development of a more customised water quality monitoring and testing regime targeting the critical pollutants. * A ‘one size fits all’ approach may not be appropriate for the different port land uses due to the varying pollutant characteristics. As such, pollution mitigation will need to be specifically tailored to suit the specific land use. * Any structural measures implemented for pollution mitigation to be effective should have the capability to remove suspended solids of size <150m. * Based on the results presented and the particularly the fact that the Port land uses cannot be compared to conventional urban land uses in relation to pollutant generation, consideration should be given to the development of a port specific water quality model.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Some applications of logic to feasibility in higher types

While it is commonly accepted that computability on a Turing machine in polynomial time represents a correct formalization of the notion of a feasibly computable function, there is no similar agreement on how to extend this notion on functionals, that is, what functionals should be considered feasible. One possible paradigm was introduced by Mehlhorn, who extended Cobham's definition of feasible functions to type 2 functionals. Subsequently, this class of functionals (with inessential changes of the definition) was studied by Townsend who calls this class POLY, and by Kapron and Cook who call the same class basic feasible functionals. Kapron and Cook gave an oracle Turing machine model characterisation of this class. In this article, we demonstrate that the class of basic feasible functionals has recursion theoretic properties which naturally generalise the corresponding properties of the class of feasible functions, thus giving further evidence that the notion of feasibility of functionals mentioned above is correctly chosen. We also improve the Kapron and Cook result on machine representation.Our proofs are based on essential applications of logic. We introduce a weak fragment of second order arithmetic with second order variables ranging over functions from NN which suitably characterises basic feasible functionals, and show that it is a useful tool for investigating the properties of basic feasible functionals. In particular, we provide an example how one can extract feasible programs from mathematical proofs that use nonfeasible functions.

Environmental factors controlling soil respiration in three semiarid ecosystems

Previous research suggests that soil organic C pools may be a feature of semiarid regions that are particularly sensitive to climatic changes. We instituted an 18-mo experiment along an elevation gradient in northern Arizona to evaluate the influence of temperature, moisture, and soil C pool size on soil respiration. Soils, from underneath different free canopy types and interspaces of three semiarid ecosystems, were moved upslope and/or downslope to modify soil climate. Soils moved downslope experienced increased temperature and decreased precipitation, resulting in decreased soil moisture and soil respiration las much as 23 acid 20%, respectively). Soils moved upslope to more mesic, cooler sites had greater soil water content and increased rates of soil respiration las much as 40%), despite decreased temperature. Soil respiration rates normalized for total C were not significantly different within any of the three incubation sites, indicating that under identical climatic conditions, soil respiration is directly related to soil C pool size for the incubated soils. Normalized soil respiration rates between sites differed significantly for all soil types and were always greater for soils incubated under more mesic, but cooler, conditions. Total soil C did not change significantly during the experiment, but estimates suggest that significant portions of the rapidly cycling C pool were lost. While long-term decreases in aboveground and belowground detrital inputs may ultimately be greater than decreased soil respiration, the initial response to increased temperature and decreased precipitation in these systems is a decrease in annual soil C efflux.

Modeling crash types: New insights into the effects of covariates on crashes at rural intersections

Many studies focused on the development of crash prediction models have resulted in aggregate crash prediction models to quantify the safety effects of geometric, traffic, and environmental factors on the expected number of total, fatal, injury, and/or property damage crashes at specific locations. Crash prediction models focused on predicting different crash types, however, have rarely been developed. Crash type models are useful for at least three reasons. The first is motivated by the need to identify sites that are high risk with respect to specific crash types but that may not be revealed through crash totals. Second, countermeasures are likely to affect only a subset of all crashes—usually called target crashes—and so examination of crash types will lead to improved ability to identify effective countermeasures. Finally, there is a priori reason to believe that different crash types (e.g., rear-end, angle, etc.) are associated with road geometry, the environment, and traffic variables in different ways and as a result justify the estimation of individual predictive models. The objectives of this paper are to (1) demonstrate that different crash types are associated to predictor variables in different ways (as theorized) and (2) show that estimation of crash type models may lead to greater insights regarding crash occurrence and countermeasure effectiveness. This paper first describes the estimation results of crash prediction models for angle, head-on, rear-end, sideswipe (same direction and opposite direction), and pedestrian-involved crash types. Serving as a basis for comparison, a crash prediction model is estimated for total crashes. Based on 837 motor vehicle crashes collected on two-lane rural intersections in the state of Georgia, six prediction models are estimated resulting in two Poisson (P) models and four NB (NB) models. The analysis reveals that factors such as the annual average daily traffic, the presence of turning lanes, and the number of driveways have a positive association with each type of crash, whereas median widths and the presence of lighting are negatively associated. For the best fitting models covariates are related to crash types in different ways, suggesting that crash types are associated with different precrash conditions and that modeling total crash frequency may not be helpful for identifying specific countermeasures.